Organizations are fragmented in their ability to understand customers, opportunities and risks. Organized by function, line of business or geography, the modern enterprise has numerous silo-based systems that are purpose-built, but inflexible in their functional evolution or compatibility with other critical systems within and outside (i.e. web sites, affiliated organization, etc.) the organization. Organizations often embark on expensive projects to leverage and correlate complex and diverse data across these systems. What is typically required in these projects is to extract, transform, and consolidate all of the remotely located data to a central point and to prepare and organize the data in one format.
This process typically involves the procurement of large central databases, middleware, data model projects, or potentially the wholesale replacement of existing point systems that expose critical data. Unfortunately, the results of these projects are usually marginal at best. At worst, these projects institutionalize poor quality, inflexibility, lack of value and unnecessary risk. Too often, because of the high cost of accessing the desired data required by the project or task, important data remains in departmental or application-based silos, preventing access to and sharing of information that should and could be used to make real time decisions. Most importantly, it has been determined that approximately $0.80 of every project dollar is spent in preparing data to run an analysis. Thus, in order to get value and business intelligence from an organization's data which may be spread out over several departments or locations, a significant upfront investment in effort, cost, and time is required.
Accordingly, what is needed is a solution that allows an organization to identify and extract valuable business data in real time over multiple platforms and locations but which does not require moving data to a central data repository but rather, which distributes the desired analytical capabilities to where the data is resident. In this manner, the data can be analyzed in its native form versus having to normalize or standardize it as is required in the prior art.